Carcinogenicity prediction of noncongeneric chemicals by augmented top priority fragment classification. (April 2016)

• Record Type: Journal Article
• Authors: Casalegno, Mosè; Sello, Guido
• Is Part Of: Computational biology and chemistry. Volume 61(2016)
• Page Start: 145
• Page End: 154
• Publication Date: 2016-04
• Subjects: Carcinogen classes -- Functional groups -- Molecular fragments -- Structural alerts -- Structure–activity relationships -- Carcinogenicity prediction; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2016.01.011
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: The prediction of chemical carcinogenicity using theoretical models is examined. A non-congeneric dataset is analysed. A blind approach to modelling carcinogenicity is used warranting an unbiased result. A comparison with available models is performed. A discussion concerning the prediction reliability is presented. Abstract: Carcinogenicity prediction is an important process that can be performed to cut down experimental costs and save animal lives. The current reliability of the results is however disputed. Here, a blind exercise in carcinogenicity category assessment is performed using augmented top priority fragment classification. The procedure analyses the applicability domain of the dataset, allocates in clusters the compounds using a leading molecular fragment, and a similarity measure. The exercise is applied to three compound datasets derived from the Lois Gold Carcinogenic Database. The results, showing good agreement with experimental data, are compared with published ones. A final discussion on our viewpoint on the possibilities that the carcinogenicity modelling of chemical compounds offers is presented.